{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "48520477",
   "metadata": {},
   "source": [
    "# Overview\n",
    "The sinking of the Titanic is one of the most infamous shipwrecks in history.\n",
    "\n",
    "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.\n",
    "\n",
    "While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n",
    "\n",
    "In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35d6be3c",
   "metadata": {},
   "source": [
    "## Data Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dde05613",
   "metadata": {},
   "source": [
    "The data has been split into two groups:\n",
    "\n",
    "> training set (train.csv) <br>\n",
    "> test set (test.csv) <br>\n",
    "\n",
    "The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.\n",
    "\n",
    "The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417560ed",
   "metadata": {},
   "source": [
    "## Data Dictionary\n",
    "\n",
    "data and its note are listed below:\n",
    "\n",
    "<center>\n",
    "<table>\n",
    "    <th>Variable</th>\t\n",
    "    <th>Definition</th>\t\n",
    "    <th>Key</th>\n",
    "    <tr>\n",
    "        <td>survival</td>\t\n",
    "        <td>Survival</td>\t\n",
    "        <td>0 = No, 1 = Yes</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>pclass\t</td>\n",
    "        <td>Ticket class </td>\t\n",
    "        <td>1 = 1st, 2 = 2nd, 3 = 3rd </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>sex\t</td>\n",
    "        <td>Sex\t</td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>Age\t</td>\n",
    "        <td>Age in years</td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>sibsp\t</td>\n",
    "        <td># of siblings / spouses aboard the Titanic\t</td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>parch\t</td>\n",
    "        <td># of parents / children aboard the Titanic\t</td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>ticket\t</td>\n",
    "        <td>Ticket number\t</td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>fare\t</td>\n",
    "        <td>Passenger fare\t</td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>cabin\t</td>\n",
    "        <td>Cabin number  </td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td>embarked\t</td>\n",
    "        <td>Port of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton </td>\n",
    "        <td> </td>\n",
    "    </tr>\n",
    "</table>\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f240b5",
   "metadata": {},
   "source": [
    "# Model Construction\n",
    "## Input Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "19b61b1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/collinsliu/jupyter/machine_learning/cases/ \n"
     ]
    }
   ],
   "source": [
    "import os.path\n",
    "\n",
    "print(os.path.abspath(\" \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2bbd2325",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "data_name = \"train.csv\"\n",
    "titanic_data = pd.read_csv(\"./titanic/\"+data_name)\n",
    "titanic_data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef124f14",
   "metadata": {},
   "source": [
    "## Data Preprocessing\n",
    "1. transforming raw data into metrics that could be used for machine learning\n",
    "2. split raw data into train set and test set, and don't touch test set until model is chosen and fine-tuned\n",
    "\n",
    "### Analysis of data\n",
    "1. first, we notice that <font color=burgendy><b>Cabin</b></font> column has many missing value. As we could simply infer, location while ship wreck toke place could greatly affect survival rate of passenger. Yet with lost data of Inner structure of Titanic and the missing of data, we decide to use other two colums of data, which are <font color=burgendy><b>Fare</b></font> and <font color=burgendy><b>Embarked</b></font>. To common sense, longer trip and higher class of cabin would be more expensive, thus we could combine those two columns to just one index, renaming it as pas_class, implying the possible class of passengers. To our anticipation, passengers of higher class would have more chance to survive."
   ]
  },
  {
   "attachments": {
    "boaring_order_time.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "2a529ca5",
   "metadata": {},
   "source": [
    "![boaring_order_time.png](attachment:boaring_order_time.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b9403f7",
   "metadata": {},
   "source": [
    " as we can see in the picture showing routine of Titanic above, the Fare in order from lower to higher should be\n",
    " $$\n",
    " C >S > Q\n",
    " $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d53de17",
   "metadata": {},
   "source": [
    "2. In our consideration, <font color=burgendy><b>Passenger name</b></font> as well as <font color=burgendy><b>Ticket number</b></font> are two index of less importance, our take is to delete them for good. Although surnames could give us considerable hints of its masters' social status, as well as the ticket number would do, for the sake of simplicity, diving into those data too deep serves no good for solving this problem."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c80b733",
   "metadata": {},
   "source": [
    "3. For the rest of index, we choose to take them all, with some transformation such as turning non-numeric data into number and implement some standardization for data integration purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bf8a5020",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import cross_val_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09df650e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3.6",
   "language": "python",
   "name": "python3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
